I have the information about the behaviour of 400 users across period of 1 months (30 days). Across those 30 days I measure 4 different information (let's call it A,B,C and D), hence I have a total of 4 time series. My goal is to predict, starting from day 5, the future values of A, by knowing the past values of A, B, C and D. So:
A(5) = f(A(1,2,3,4), B(1,2,3,4), C(1,2,3,4), D(1,2,3,4))
Ideally, I'd like to estimate the whole future values, from day 5 to day 30.
I tried to use LSTM by using as input the past series, to predict the future trends. However, I am not getting great results, and I am hoping I can use a simpler approach. I came across with the concept of Kalman Filter, where the value at time step k can be estimated by using the past values. Here there are some nice examples , however I have no idea where to start from. Would it fit with my problem, if yes, how could I fit my past values into the equation for the estimation of A(k)? Any suggestion is appreciated.